SoroushVahidi/scidocs huggingface dataset:
Physical and mental health effects of intimate partner violence for men and women.
(2002)

BACKGROUND
Few population-based studies have assessed the physical and mental health consequences of both psychological and physical intimate partner violence (IPV) among women or men victims. This study estimated IPV prevalence by type (physical, sexual, and psychological) and associated physical and mental health consequences among women and men.


METHODS
The study analyzed data from the National Violence Against Women Survey (NVAWS) of women and men aged 18 to 65. This random-digit-dial telephone survey included questions about violent victimization and health status indicators.


RESULTS
A total of 28.9% of 6790 women and 22.9% of 7122 men had experienced physical, sexual, or psychological IPV during their lifetime. Women were significantly more likely than men to experience physical or sexual IPV (relative risk [RR]=2.2, 95% confidence interval [CI]=2.1, 2.4) and abuse of power and control (RR=1.1, 95% CI=1.0, 1.2), but less likely than men to report verbal abuse alone (RR=0.8, 95% CI=0.7, 0.9). For both men and women, physical IPV victimization was associated with increased risk of current poor health; depressive symptoms; substance use; and developing a chronic disease, chronic mental illness, and injury. In general, abuse of power and control was more strongly associated with these health outcomes than was verbal abuse. When physical and psychological IPV scores were both included in logistic regression models, higher psychological IPV scores were more strongly associated with these health outcomes than were physical IPV scores.


CONCLUSIONS
Both physical and psychological IPV are associated with significant physical and mental health consequences for both male and female victims.
Ship Detection From Optical Satellite Images Based on Saliency Segmentation and Structure-LBP Feature
(2017)

Automatic ship detection from optical satellite imagery is a challenging task due to cluttered scenes and variability in ship sizes. This letter proposes a detection algorithm based on saliency segmentation and the local binary pattern (LBP) descriptor combined with ship structure. First, we present a novel saliency segmentation framework with flexible integration of multiple visual cues to extract candidate regions from different sea surfaces. Then, simple shape analysis is adopted to eliminate obviously false targets. Finally, a structure-LBP feature that characterizes the inherent topology structure of ships is applied to discriminate true ship targets. Experimental results on numerous panchromatic satellite images validate that our proposed scheme outperforms other state-of-the-art methods in terms of both detection time and detection accuracy.
Matching and Retrieval of Distorted and Occluded Shapes Using Dynamic Programming
(2002)

We propose an approach for matching distorted and possibly occluded shapes using Dynamic Programming (DP). We distinguish among various cases of matching such as cases where the shapes are scaled with respect to each other and cases where an open shape matches the whole or only a part of another open or closed shape. Our algorithm treats noise and shape distortions by allowing matching of merged sequences of consecutive small segments in a shape with larger segments of another shape, while being invariant to translation, scale, orientation and starting point selection. We illustrate the effectiveness of our algorithm in retrieval of shapes on two datasets of two-dimensional open and closed shapes of marine life species. We demonstrate the superiority of our approach over traditional approaches to shape matching and retrieval based on Fourier descriptors and moments. We also compare our method with SQUID, a well known method which is available on the Internet. Our evaluation is based on human relevance judgments following a well-established methodology from the information retrieval field. A preliminary version of this work was presented at the Int. Conf. on Pattern Recognition, Barcelona, Spain, pages 67-71, Vol. 4, Sept. 2000. Corresponding author. Department of Electronic and Computer Engineering, Technical University of Crete, Chania, Crete, GR-73100, Greece, E-mail: petrakis@ced.tuc.gr, URL: http://www.ced.tuc.gr/ ̃petrakis, Intelligent Sensory Information Systems, Faculty of Science, University of Amsterdam, Kruislaan 403, 1098 SJ Amsterdam, The Netherlands. E-mail: diplaros@science.uva.nl. This work is part of the author’s student dissertation at the Department of Electronic and Computer Engineering of the Technical University of Crete. Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada B3H 1W5. E-mail: eem@cs.dal.ca, URL: http://www.cs.dal.ca/ ̃eem.
Curlybot: Designing a New Class of Computational Toys
(2000)

We introduce an educational toy, called curlybot, as the basis for a new class of toys aimed at children in their early stages of development — ages four and up. curlybot is an autonomous two-wheeled vehicle with embedded electronics that can record how it has been moved on any flat surface and then play back that motion accurately and repeatedly. Children can use curlybot to develop intuitions for advanced mathematical and computational concepts, like differential geometry, through play away from a traditional computer.
In our preliminary studies, we found that children learn to use curlybot quickly. They readily establish an affective and body syntonic connection with curlybot, because of its ability to remember all of the intricacies of their original gesture; every pause, acceleration, and even the shaking in their hand is recorded. Programming by example in this context makes the educational ideas implicit in the design of curlybot accessible to young children.
Clustering of contingency table and mixture model
(2007)

Basing cluster analysis on mixture models has become a classical and powerful approach. It enables some classical criteria such as the well-known k-means criterion to be explained. To classify the rows or the columns of a contingency table, an adapted version of k-means known as Mndki2, which uses the chi-square distance, can be used. Unfortunately, this simple, effective method which can be used jointly with correspondence analysis based on the same representation of the data, cannot be associated with a mixture model in the same way as the classical k-means algorithm. In this paper we show that the Mndki2 algorithm can be viewed as an approximation of a classifying version of the EM algorithm for a mixture of multinomial distributions. A comparison of the algorithms belonging in this context are experimentally investigated using Monte Carlo simulations. 2006 Elsevier B.V. All rights reserved.
Multicriteria classification and sorting methods: A literature review
(2002)

The assignment of alternatives (observations/objects) into predefined homogenous groups is a problem of major practical and research interest. This type of problem is referred to as classification or sorting, depending on whether the groups are nominal or ordinal. Methodologies for addressing classification and sorting problems have been developed from a variety of research disciplines, including statistics/econometrics, artificial intelligent and operations research. The objective of this paper is to review the research conducted on the framework of the multicriteria decision aiding (MCDA). The review covers different forms of MCDA classification/sorting models, different aspects of the model development process, as well as real-world applications of MCDA classification/sorting techniques and their software implementations. 2002 Elsevier Science B.V. All rights reserved.
Modeling Driver Behavior in a Cognitive Architecture
(2006)

OBJECTIVE
This paper explores the development of a rigorous computational model of driver behavior in a cognitive architecture--a computational framework with underlying psychological theories that incorporate basic properties and limitations of the human system.


BACKGROUND
Computational modeling has emerged as a powerful tool for studying the complex task of driving, allowing researchers to simulate driver behavior and explore the parameters and constraints of this behavior.


METHOD
An integrated driver model developed in the ACT-R (Adaptive Control of Thought-Rational) cognitive architecture is described that focuses on the component processes of control, monitoring, and decision making in a multilane highway environment.


RESULTS
This model accounts for the steering profiles, lateral position profiles, and gaze distributions of human drivers during lane keeping, curve negotiation, and lane changing.


CONCLUSION
The model demonstrates how cognitive architectures facilitate understanding of driver behavior in the context of general human abilities and constraints and how the driving domain benefits cognitive architectures by pushing model development toward more complex, realistic tasks.


APPLICATION
The model can also serve as a core computational engine for practical applications that predict and recognize driver behavior and distraction.
P-Rank: An indicator measuring prestige in heterogeneous scholarly networks
(2011)

Ranking scientific productivity and prestige are often limited to homogeneous networks. These networks are unable to account for the multiple factors that constitute the scholarly communication and reward system. This study proposes a new informetric indicator, PRank, for measuring prestige in heterogeneous scholarly networks containing articles, authors, and journals. P-Rank differentiates the weight of each citation based on its citing papers, citing journal, and citing authors. Articles from 16 representative library and information science journals are selected as the dataset. Principle Component Analysis is conducted to examine the relationship between P-Rank and other bibliometric indicators. We also compare the correlation and rank variances between citation counts and P-Rank scores. This work provides a new approach to examining prestige in scholarly communication networks in a more comprehensive and nuanced way.
A Priority based Round Robin CPU Scheduling Algorithm for Real Time Systems
(2012)

The main objective of this paper is to develop a new approach for round robin C P U scheduling a l g o r i t h m which improves the performance of CPU in real time operating system. The proposed Priority based Round-Robin CPU Scheduling algorithm is based on the integration of round-robin and priority scheduling algorithm. It retains the advantage of round robin in reducing starvation and also integrates the advantage of priority scheduling. The proposed algorithm also implements the concept of aging by assigning new priorities to the processes. Existing round robin CPU scheduling algorithm cannot be implemented in real time operating system due to their high context switch rates, large waiting time, large response time, large turnaround time and less throughput. The proposed algorithm improves all the drawbacks of round robin C P U scheduling algorithm. The paper also presents the comparative analysis of proposed algorithm with existing round robin scheduling algorithm on the basis of varying time quantum, average waiting time, average turnaround time and number of context switches.
A New Round Robin Based Scheduling Algorithm for Operating Systems: Dynamic Quantum Using the Mean Average
(2011)

Round Robin, considered as the most widely adopted CPU scheduling algorithm, undergoes severe problems directly related to quantum size. If time quantum chosen is too large, the response time of the processes is considered too high. On the other hand, if this quantum is too small, it increases the overhead of the CPU. In this paper, we propose a new algorithm, called AN, based on a new approach called dynamic-time-quantum; the idea of this approach is to make the operating systems adjusts the time quantum according to the burst time of the set of waiting processes in the ready queue. Based on the simulations and experiments, we show that the new proposed algorithm solves the fixed time quantum problem and increases the performance of Round Robin.
FINDING TIME QUANTUM OF ROUND ROBIN CPU SCHEDULING ALGORITHM IN GENERAL COMPUTING SYSTEMS USING INTEGER PROGRAMMING
(2010)

In Round Robin Scheduling, the time quantum is fixed and then processes are scheduled such that no process get CPU time more than one time quantum in one go. If time quantum is too large, the response time of the processes is too much which may not be tolerated in interactive environment. If time quantum is too small, it causes unnecessarily frequent context switch leading to more overheads resulting in less throughput. In this paper, a method using integer programming has been proposed to solve equations that decide a value that is neither too large nor too small such that every process has reasonable response time and the throughput of the system is not decreased due to unnecessarily context switches.
Schedulability analysis of periodic fixed priority systems
(2004)

Feasibility analysis of fixed priority systems has been widely studied in the real-time literature and several acceptance tests have been proposed to guarantee a set of periodic tasks. They can be divided in two main classes: polynomial time tests and exact tests. Polynomial time tests can efficiently be used for online guarantee of real-time applications, where tasks are activated at runtime. These tests introduce a negligible overhead, when executed upon a new task arrival, however provide only a sufficient schedulability condition, which may cause a poor processor utilization. On the other hand, exact tests, which are based on response time analysis, provide a necessary and sufficient schedulability condition, but are too complex to be executed on line for large task sets. As a consequence, for large task sets, they are often executed off line. This paper proposes a novel approach for analyzing the schedulability of periodic task sets on a single processor under an arbitrary fixed priority assignment: Using this approach, we derive a new schedulability test which can be tuned through a parameter to balance complexity versus acceptance ratio, so that it can be used on line to better exploit the processor, based on the available computational power. Extensive simulations show that our test, when used in its exact form, is significantly faster than the current response time analysis methods. Moreover the proposed approach, for its elegance and compactness, offers an explanation of some known phenomena of fixed priority scheduling and could be helpful for further work on schedulability analysis.
An Optimized Round Robin Scheduling Algorithm for CPU Scheduling
(2010)

The main objective of this paper is to develop a new approach for round robin scheduling which help to improve the CPU efficiency in real time and time sharing operating system. There are many algorithms available for CPU scheduling. But we cannot implemented in real time operating system because of high context switch rates, large waiting time, large response time, large trn around time and less throughput. The proposed algorithm improves all the drawback of simple round robin architecture. The author have also given comparative analysis of proposed with simple round robin scheduling algorithm. Therefore, the author strongly feel that the proposed architecture solves all the problem encountered in simple round robin architecture by decreasing the performance parameters to desirable extent and thereby increasing the system throughput.
BranchGAN: Branched Generative Adversarial Networks for Scale-Disentangled Learning and Synthesis of Images.
(2018)

We introduce BranchGAN , a novel training method that enables unconditioned generative adversarial networks (GANs) to learn image manifolds at multiple scales. The key novel feature of BranchGAN is that it is trained in multiple branches, progressively covering both the breadth and depth of the network, as resolutions of the training images increase to reveal finer-scale features. Specifically, each noise vector, as input to the generator network, is explicitly split into several sub-vectors, each corresponding to, and is trained to learn, image representations at a particular scale. During training, we progressively “defreeze” the sub-vectors, one at a time, as a new set of higher-resolution images is employed for training and more network layers are added. A consequence of such an explicit sub-vector designation is that we can directly manipulate and even combine latent (sub-vector) codes which model different feature scales. Experiments demonstrate the effectiveness of our training method in scale-disentangled learning of image manifolds and synthesis, without any extra labels and without compromising quality of the synthesized high-resolution images. We further demonstrate three applications enabled or improved by BranchGAN.
Underground Mine Communications: A Survey
(2009)

After a recent series of unfortunate underground mining disasters, the vital importance of communications for underground mining is underlined one more time. Establishing reliable communication is a very difficult task for underground mining due to the extreme environmental conditions. Until now, no single communication system exists which can solve all of the problems and difficulties encountered in underground mine communications. However, combining research with previous experiences might help existing systems improve, if not completely solve all of the problems. In this survey, underground mine communication is investigated. Major issues which underground mine communication systems must take into account are discussed. Communication types, methods, and their significance are presented.
Heteroscedastic Gaussian process regression
(2005)

This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametric regression problem. The key point is that we are able to estimate variance locally unlike standard Gaussian Process regression or SVMs. This means that our estimator adapts to the local noise. The problem is cast in the setting of maximum a posteriori estimation in exponential families. Unlike previous work, we obtain a convex optimization problem which can be solved via Newton's method.
Group Buying on the Web: A Comparison of Price-Discovery Mechanisms
(2003)

Web-based Group-Buying mechanisms, a refinement of quantity discounting, are being used for both Business-to-Business (B2B) and Business-toConsumer (B2C) transactions. In this paper, we survey currently operational online Group-Buying markets, and then study this phenomenon using analytical models. We surveyed over fifty active Group-Buying sites, and provide a comprehensive review of Group-Buying practices in the B2B, B2C and non-profit sectors, across three continents. On the modeling side, we build on the coordination literature in Information Economics and the quantity-discounts literature in Operations to develop an analytical model of a monopolist who uses web-based Group-Buying mechanisms under different kinds of demand uncertainty. We derive the monopolist's optimal Group-Buying schedule, and compare his profits with those that obtain under the more conventional posted-price mechanism. We also study the effect of heterogeneity in the demand regimes, in combination with uncertainty, on the relative performance of the two mechanisms. We further study the impact of the timing of the pricing decision (vis-à-vis the production decision) by modeling it as a two-stage game between the monopolist and buyers. Finally, we investigate how Group-Buying schemes compare with posted price markets when buyers can revise their prior valuation of products based on information received from third parties (infomediaries). In all cases, we characterize the conditions under which one mechanism outperforms the other, and those under which the posted price and Group-Buy mechanisms lead to identical seller revenues. Our results have implications for firms' choice of price discovery mechanisms in electronic markets and scheduling of production and pricing decisions in the presence (and absence) of scale economies of production. 1 Email: anandk@wharton.upenn.edu; Tel: (215) 898 1175. 2 Email: raviaron@wharton.upenn.edu; Tel:(215) 573 5677.
Tamperproof transmission of fingerprints using visual cryptography schemes
(2010)

Visual Cryptography and biometrics have been identified as the two most important aspects of digital security. In this paper, we propose a method for the preparation and secure transmission of fingerprint images using visual cryptography scheme. Visual cryptography (VC) is a kind of secret image sharing scheme that uses the human visual system to perform the decryption computations. A visual cryptography scheme (VCS) allows confidential messages to be encrypted into k-out-of-n secret sharing schemes. Whenever the number of participants from the group (n) is larger than or equal to the predetermined threshold value (k), the confidential message can be obtained by these participants. VCS is interesting because decryption can be done with no prior knowledge of cryptography and can be performed without any cryptographic computations. In this paper, the fingerprint image is broken up into n pieces, which individually yield no information about the image. These pieces of the images, called shares or shadows, may then be distributed among a group of n participants/dealers. By combining any k (k ≤ n) of these shares, the original fingerprint image can be recovered, but combining less than k of them will not reveal any information about the image. The scheme is perfectly secure and very easy to implement. To decode the encrypted information, i.e., to get the original information back, the shares are stacked and the secret image pops out. The only drawback of the VCS is the loss in contrast of reconstructed image. The proposed method for the reconstruction of fingerprint image is based on XNOR operation. This will enable one to obtain back perfect fingerprint image.
Consumer buying behavior towards online shopping stores in Malaysia
(2012)

The global nature of communication and shopping has as well redefined, seeing that it is the perfect vehicle for online shopping stores. Online convenient shop is mostly reflected in shorter time and less energy spent, including shipping cost reduction, less crowd and queues than real markets, unlimited time and space, which all increase convenience of shopping. Internet shopping for businesses and consumers are being accepted as an alternative shop mode rather than visiting the stores. However, convincing the consumers to shop online is still a challenging task for web retailers in Malaysia. The growth of Internet technology in Malaysia has enormous potential as it reduces the costs of product and service delivery and extends geographical boundaries in bringing buyers and sellers together. This study is conducted to identify factors influencing consumers towards online shopping in Malaysia. The study focused on nine independent variables namely appearance, quick loading, security, sitemap, validity, promotion, attractiveness, believability, and originality. We applied Five-point Likert Scale to measure the influential factors on intention for online shopping.